A Multidimensional Poverty Analysis: Evidence from Lebanese Data
Ahmad Ashaal1, Ahmed Bakri2

1Ahmad Ashaal, Lecturer, Lebanese International University, Lebanon.
2Ahmed Bakri, Chairperson of Finance, Lebanese International University, Lebanon.
Manuscript received on 08 May 2019 | Revised Manuscript received on 19 May 2019 | Manuscript Published on 23 May 2019 | PP: 1099-1114 | Volume-7 Issue-6S5 April 2019 | Retrieval Number: F11880476S519/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Traditionally, empirical studies on poverty have relied on income and monetary indicators to identify the poor in the population. Specifically, the poor are defined as those people whose income is below a certain threshold, normally set at a certain percentage of the average or median income of the society. A variety of alternative approaches have been put forward to overcome such critiques. However, the adoption of a broader set of information relating, for instance, to the ownership of consumer goods or to the access to various goods and services, raises the complex issue of deriving measures of standard of living that are of a multidimensional nature. The approach we propose aims at developing multidimensional measures of poverty at the level of Lebanon, using information contained in the first wave of the Lebanese component of the Statistics on Income and Living Conditions. Along the lines suggested by other studies, we have derived deprivation indices on the basis of direct, non-monetary standard of living indicators. As a second step, the relationship between income and non-income indicators of poverty in Lebanon has been investigated, in order to examine to what extent alternative, multidimensional measures could be combined with income to better identify the poor. Our results confirm the common finding that income-indicators are able to measure poverty only to a certain degree. This study appears to be particularly relevant for the Lebanese case. Firstly, it provides empirical support to the opportunity of supplement income-based measures with additional non-monetary information, by showing to what extent the main results obtained for other countries may hold also for Lebanon. Secondly, an analysis of poverty based on information different from income may be particularly useful for a country such as Lebanon, where income data are not very reliable, because of underreporting and tax evasion problems.
Keywords: Data Analysis Evidence Measures Information.
Scope of the Article: Data Mining